Proper development of hydrocarbon reservoirs requires accurate reservoir simulation. Accurate reservoir simulation may be achieved with proper modeling of the porosity and the connectivity of the pore structure of the rocks that form the reservoir at different scales, including grain-pore levels. The porosity distribution and its connectivity will affect not only the amount of hydrocarbons in the reservoir, but also how readily the hydrocarbons may flow through the reservoir.
In rocks that make up a hydrocarbon reservoir, grain minerals are surrounded by open space in the form of interconnected pores or a pore network. The texture of the pore network, which includes connectivity, range of pore sizes and average pore size, is a function of the type of rock. Pore sizes can range in size from a few millimeters to a few nanometers. Some rocks have a narrow range of pore sizes, such as clastic rocks with well sorted grains, while others have a wide range, such as some carbonate rocks which may have both millimeter size vugs and micro-porosity too (pores with diameters between 10-100 nanometers). High resolution 2D imaging techniques, such as Back Scattering Electron Microscopy (BSEM), can image down to 10 nanometer resolution, but the connectivity of the pore network needs to be characterized in 3D.
One tool that can be used to generate data that can be used to characterize the pore connectivity of a rock sample is 3D x-ray micro-tomography. X-ray tomography images are monochromatic and the local intensity in the images is proportional to the local density of the material. In x-ray tomography, an x-ray source, a rotation stage and a detector are used to create 2D projections at several orientations of the rock sample respect to the source-detector line, and then a reconstruction algorithm is used to produce a 3D density volume. This is a non-invasive technique, and the data generated is typically noisy depending on the flux of detected photons, particularly if the scanning is done too quickly, if the rock is particularly dense, or if the quality of the x-ray source is not adequate. The resolution of the images or pixel size depends on the spot-size of the x-ray source and on the geometrical or optical magnification system used on the micro-tomography scanner. Typically, the spatial resolution on micro-tomography using geometrical magnification is 2-3 μm/pixel, while systems using optical magnification can go down to about 0.5 μm/pixel.
At any of these resolutions, there may be still some un-resolvable features, such as micro-porosity in carbonates or in clay. In order to model pore connectivity, it is required to segment or identify the pore pixels. However, pixels containing sub-resolution pores cannot be labeled pore, but can be label “sub-resolution porosity” and a micro-porosity value can be associated with the intermediate grey in the intensity images. Other phases of interest can also be segmented based on the grey intensity of the images, such as fluids (brine, oil), bitumen, and relevant minerals (clays, feldspars, etc). Current segmentation methods of the 3D x-ray micro-tomography images into phases are limited mainly by the interplay between intensity contrast for phases to be segmented and the signal-to-noise level of the data.
Current segmentation methods that focus on porosity characterization include 2-phase segmentations (pore/solid), and 3-phase segmentations (pore/sub-resolution-porosity/solid) using thresholds, smoothing filters, and morphological transformations (watershed, active contour, dilation/erosion methods). The 2-phase segmentation method divides the images into solid or pore pixels, erroneously labeling the sub-resolution porosity pixels as pore or solid, therefore, accounting only for pores of resolvable size. Current 2-phase and 3-phase segmentation using thresholds determined from the x-ray tomography images result in segmented images that have a characteristic “salt-and-pepper” noise that can be minimized by applying smoothing algorithms prior to segmentation, such as mean/median filters and anisotropy diffusion filters. The smoothing filters effectively reduce the spatial resolution of the image because they mix information of nearby pixels, which results in increasing the total sub-resolution porosity fraction. Additional lost of resolution and smoothing results from the application of subsequent morphological transformations in the current methods. Sometimes a distinguishable artificial length scale can be introduced when using smoothing filters. Smoothing can also underestimate inter-phase surface roughness and affect final simulation results, such as increasing fluid flow permeability.
For these reasons, current segmentation methods cannot be relied on to properly segment the x-ray tomography images into a representation that accurately depicts the resolvable porosity, the un-resolvable porosity, and the solid phases at the original pixel resolution of the tomogram; current methods have a larger, poorer resolution. With these inaccurate segmentation methods, the resulting models for connectivity of the pore networks may not be precise enough, and scaled properties used in reservoir simulation will not be correct.